Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features

Salman Khan; Mukhtaj Khan; Nadeem Iqbal; Naqqash Dilshad; Maram Fahaad Almufareh; Najah Alsubaie

doi:10.3390/life13112153

Enhancing Sumoylation Site Prediction: A Deep Neural Network with Discriminative Features

Life (Basel). 2023 Nov 2;13(11):2153. doi: 10.3390/life13112153.

Authors

Salman Khan¹, Mukhtaj Khan², Nadeem Iqbal¹, Naqqash Dilshad³, Maram Fahaad Almufareh⁴, Najah Alsubaie⁵

Affiliations

¹ Department of Computer Science, Abdul Wali Khan University, Mardan 23200, Pakistan.
² Department of Information Technology, The University of Haripur, Haripur 22620, Pakistan.
³ Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea.
⁴ Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
⁵ Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Abstract

Sumoylation is a post-translation modification (PTM) mechanism that involves many critical biological processes, such as gene expression, localizing and stabilizing proteins, and replicating the genome. Moreover, sumoylation sites are associated with different diseases, including Parkinson's and Alzheimer's. Due to its vital role in the biological process, identifying sumoylation sites in proteins is significant for monitoring protein functions and discovering multiple diseases. Therefore, in the literature, several computational models utilizing conventional ML methods have been introduced to classify sumoylation sites. However, these models cannot accurately classify the sumoylation sites due to intrinsic limitations associated with the conventional learning methods. This paper proposes a robust computational model (called Deep-Sumo) for predicting sumoylation sites based on a deep-learning algorithm with efficient feature representation methods. The proposed model employs a half-sphere exposure method to represent protein sequences in a feature vector. Principal Component Analysis is applied to extract discriminative features by eliminating noisy and redundant features. The discriminant features are given to a multilayer Deep Neural Network (DNN) model to predict sumoylation sites accurately. The performance of the proposed model is extensively evaluated using a 10-fold cross-validation test by considering various statistical-based performance measurement metrics. Initially, the proposed DNN is compared with the traditional learning algorithm, and subsequently, the performance of the Deep-Sumo is compared with the existing models. The validation results show that the proposed model reports an average accuracy of 96.47%, with improvement compared with the existing models. It is anticipated that the proposed model can be used as an effective tool for drug discovery and the diagnosis of multiple diseases.

Keywords: artificial intelligence; deep neural network; half-sphere exposure; machine-learning algorithm; sumoylation sites.

Grants and funding

PNURSP2023R321/This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R321), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.